This project is focused on the development, implementation, validation and distribution of computational tools for the analysis of MEG and EEC (E/MEG) data for the purpose of developing an increased understand- ing of normal and pathological function in the human brain. Over the past four years we have made progress in the following areas: (i) the development of tools for the analysis of anatomical MR data for use in localizing E/MEG sources;(ii)computationally efficient and atlas-based forward models;(iii)parametric and image- based inverse MEG and EEG methods;(iv) statistical analysis of parametric and image-based inverse solu- tions;(v) software development, distribution and support;(vi) validation with both simulated and in vivo data; and (vi) applications to both epilepsy and cognitive neuroscientific data. This work focused primarily on analy- sis of event-related averaged data in single subjects. Statistical analysis was restricted to pair-wise compari- sons. For the next project period our goal is to extend our inverse approaches to investigate both evoked and induced responses using imaging and parametric methods and to extend our statistical tools to allow analysis of experimental data involving multiple conditions in individuals and groups. We will also develop tools to investigate interactions between brain regions and again develop statistical tools to assess the significance of these models. These methods will be validated using computer simulation and in vivo studies involving visu- ally cued attention paradigms in normal subjects. The specific aims that we will address in developing and evaluating these tools are as follows: (1) Inverse Methods: development of linear imaging methods optimized for maximally isotropic and shift invariant resolution, cortically constrained dipole and multipole fits for estima- tion of multiple focal sources, and beamforming methods for simultaneous monitoring of multiple distinct corti- cal regions;(2) Cortical Alignment: development of tools for coregistering cortical surfaces using covariant PDEs based on alignment of sulcal features and minimization of a thin plate spline bending energy in the intrinsic geometry of the cortical surface;(3) Statistical Analysis: development of nonparametric methods for univariate analysis of cortical maps of induced and evoked responses in individuals and groups;(4) develop- ment of models of large scale cortical interactions using broadband linear and nonlinear methods based, respectively, on the multichannel autoregressive model and bispectral analysis;(5) distribution of software implementations of the methods in Aims 1-4through continued development of the Brainstorm and BrainSuite software tools;and (6) application of all of the above methods to the analysis of experimental high density EEG and MEG data from a visually cued attention study to investigate the ability of the methods developed in Aims 1-4 to extract a meaningful and consistent picture of brain activation and communication.